/** * Evaluates the supplied prediction on a single instance. * * @param prediction the supplied prediction * @param instance the test instance to be classified * @throws Exception if model could not be evaluated successfully */ public void evaluateModelOnce(double prediction, Instance instance) throws Exception { m_delegate.evaluateModelOnce(prediction, instance); }
m_eval.evaluateModelOnceAndRecordPrediction(m_classifier, inst); } else { m_eval.evaluateModelOnce(m_classifier, inst);
test.instance(i)); } else { m_eval.evaluateModelOnce(m_classifier, test.instance(i));
/** * Evaluates the supplied distribution on a single instance. * * @param dist the supplied distribution * @param instance the test instance to be classified * @return the prediction * @throws Exception if model could not be evaluated successfully */ public double evaluateModelOnce(double[] dist, Instance instance) throws Exception { return m_delegate.evaluateModelOnce(dist, instance); }
m_eval.evaluateModelOnceAndRecordPrediction(dist, instance); } else { m_eval.evaluateModelOnce(dist, instance); m_windowEval.evaluateModelOnce(dist, instance); m_window.addFirst(instance); m_windowedPreds.addFirst(dist); m_windowEval.evaluateModelOnce(oldDist, oldest); oldest.setWeight(-oldest.weight());
/** * Evaluates the supplied distribution on a single instance. * * @param dist the supplied distribution * @param instance the test instance to be classified * @return the prediction * @throws Exception if model could not be evaluated successfully */ public double evaluateModelOnce(double[] dist, Instance instance) throws Exception { return m_delegate.evaluateModelOnce(dist, instance); }
m_eval.evaluateModelOnceAndRecordPrediction(dist, instance); } else { m_eval.evaluateModelOnce(dist, instance); m_windowEval.evaluateModelOnce(dist, instance); m_window.addFirst(instance); m_windowedPreds.addFirst(dist); m_windowEval.evaluateModelOnce(oldDist, oldest); oldest.setWeight(-oldest.weight());
/** * Evaluates the supplied prediction on a single instance. * * @param prediction the supplied prediction * @param instance the test instance to be classified * @throws Exception if model could not be evaluated successfully */ public void evaluateModelOnce(double prediction, Instance instance) throws Exception { m_delegate.evaluateModelOnce(prediction, instance); }
/** * Evaluates the classifier on a single instance. * * @param classifier machine learning classifier * @param instance the test instance to be classified * @return the prediction made by the clasifier * @throws Exception if model could not be evaluated successfully or the data * contains string attributes */ public double evaluateModelOnce(Classifier classifier, Instance instance) throws Exception { return m_delegate.evaluateModelOnce(classifier, instance); }
/** * Evaluates the classifier on a single instance. * * @param classifier machine learning classifier * @param instance the test instance to be classified * @return the prediction made by the clasifier * @throws Exception if model could not be evaluated successfully or the data * contains string attributes */ public double evaluateModelOnce(Classifier classifier, Instance instance) throws Exception { return m_delegate.evaluateModelOnce(classifier, instance); }
/** * Evaluates the supplied prediction on a single instance. * * @param prediction the supplied prediction * @param instance the test instance to be classified * @throws Exception if model could not be evaluated successfully */ public void evaluateModelOnce(double prediction, Instance instance) throws Exception { evaluateModelOnce(makeDistribution(prediction), instance); }
/** * Evaluates the supplied prediction on a single instance. * * @param prediction the supplied prediction * @param instance the test instance to be classified * @throws Exception if model could not be evaluated successfully */ public void evaluateModelOnce(double prediction, Instance instance) throws Exception { evaluateModelOnce(makeDistribution(prediction), instance); }